Research Engineer
Felippe Schmoeller da Roza studied electrical engineering and has been working as a Research Engineer at Fraunhofer IKS since January 2020. As a control engineer, he has research experience in mobile robotics with a focus on control, computer vision and navigation. At Fraunhofer IKS, he works on the development of safe adaptive systems, using concepts of reinforcement learning, deep learning, and control theory.
by Felippe Schmoeller da Roza
Reinforcement learning (RL) is one of three main machine learning (ML) paradigms. Unlike the other two, supervised learning and unsupervised learning, in reinforcement learning the learning takes place in direct interaction with the surroundings. A brief introduction follows.
Achieving safety with AI systems requires a comprehensive and collaborative approach, including technical, ethical, and regulatory considerations. A central aspect is designing AI systems that comply with safety requirements defined in constraints.
Deep learning has boosted the interest in deploying artificial intelligence (AI) to more systems. Reinforcement learning (RL) has also been through a revival, with Deep RL models designed to play Atari games and even beat grandmasters at the game of Go. However, despite these recently obtained impressive achievements, RL is still struggling to be accepted and successfully deployed in some applications, especially in the case of safety-critical tasks such as robotics, autonomous driving and industrial control.
Deep Neural Networks (DNNs) have proven their potential. This applies above all to their use in the laboratory. Their performance in real-world applications leaves a lot to be desired. One major problem is the estimation of uncertainty. A closer look at methods that meet this challenge.
